Noninvasive measurement of glucose through the optical properties of tissue

ABSTRACT

Methods and apparatus for noninvasive determination of blood analytes, such as glucose, through NIR spectroscopy utilize optical properties of tissue as reflected in key spectroscopic features to improve measurement accuracy and precision. Physiological conditions such as changes in water distribution among tissue compartments lead to complex alterations in the measured absorbance spectrum of skin and reflect a modification in the effective pathlength of light, leading to a biased noninvasive glucose measurement. Changes in the optical properties of tissue are detected by identifying key features responsive to physiological variations. Conditions not conducive to noninvasive measurement of glucose are detected. Noninvasive glucose measurements that are biased by physiological changes in tissue are compensated. In an alternate embodiment, glucose is measured indirectly based on natural physiological response of tissue to glucose concentration. A spectroscopic device capable of such measurements is provided.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates generally to noninvasive tissueanalyte determination. More particularly, the invention relates tomethods and apparatus for characterizing physiological and chemicalproperties of an irradiated tissue sample by extracting spectralfeatures reflecting optical properties of key tissue constituents.Subsequently, based on such spectral features, noninvasive glucosemeasurements that are biased by physiological changes in tissue arecompensated. Alternatively, glucose is measured indirectly based onnatural physiological response of tissue to shifts in glucoseconcentration.

[0003] 2. Description of Related Art

[0004] Noninvasive Measurement of Glucose

[0005] Diabetes is a leading cause of death and disability worldwide andafflicts an estimated 16 million Americans. Complications of diabetesinclude heart and kidney disease, blindness, nerve damage and high bloodpressure with the estimated total cost to United States economy aloneexceeding $90 billion per year [Diabetes Statistics, Publication No.98-3926, National Institutes of Health, Bethesda Md. (November 1997)].Long-term clinical studies show that the onset of complications can besignificantly reduced through proper control of blood glucose levels[The Diabetes Control and Complications Trial Research Group, The effectof intensive treatment of diabetes on the development and progression oflong-term complications in insulin-dependent diabetes mellitus. N Eng Jof Med. 329:977-86 (1993)]. A vital element of diabetes management isthe self-monitoring of blood glucose levels by diabetics in the homeenvironment. A significant disadvantage of current monitoring techniquesis that they discourage regular use due to the inconvenient and painfulnature of drawing blood through the skin prior to analysis. Therefore,new methods for self-monitoring of blood glucose levels are required toimprove the prospects for more rigorous control of blood glucose indiabetic patients.

[0006] Numerous approaches have been explored for measuring bloodglucose levels, ranging from invasive methods such as microdialysis tononinvasive technologies that rely on spectroscopy. Each method hasassociated advantages and disadvantages, but only a few have receivedapproval from certifying agencies. To date, no noninvasive techniquesfor the self-monitoring of blood glucose have been certified.

[0007] One method, near-infrared spectroscopy involves the illuminationof a spot on the body with near-infrared electromagnetic radiation(light in the wavelength range 750-2500 nm). The light is partiallyabsorbed and scattered, according to its interaction with theconstituents of the tissue prior to being reflected back to a detector.The detected light contains quantitative information that is based onthe known interaction of the incident light with components of the bodytissue including water, fat, protein and glucose.

[0008] Previously reported methods for the noninvasive measurement ofglucose through near-infrared spectroscopy rely on the detection of themagnitude of light attenuation caused by the absorption signature ofblood glucose as represented in the targeted tissue volume. The tissuevolume is the portion of irradiated tissue from which light is reflectedor transmitted to the spectrometer detection system. The signal due tothe absorption of glucose is extracted from the spectral measurementthrough various methods of signal processing and one or moremathematical models. The models are developed through the process ofcalibration on the basis of an exemplary set of spectral measurementsand associated reference blood glucose values (the calibration set)based on an analysis of capillary (fingertip) or venous blood.

[0009] Near-infrared spectroscopy has been demonstrated in specificstudies to represent a feasible and promising approach to thenoninvasive prediction of blood glucose levels. M. Robinson, R. Eaton,D. Haaland, G. Keep, E. Thomas, B. Stalled, P. Robinson, Noninvasiveglucose monitoring in diabetic patients: A preliminary evaluation, ClinChem, 38:1618-22 (1992) reports three different instrumentconfigurations for measuring diffuse transmittance through the finger inthe 600-1300 nm range. Meal tolerance tests were used to perturb theglucose levels of three subjects and calibration models were constructedspecific to each subject on single days and tested throughcross-validation. Absolute average prediction errors ranged from 19.8 to37.8 mg/dl. H. Heise, R. Marbach, T. Koschinsky, F. Gries, Noninvasiveblood glucose sensors based on near-infrared spectroscopy, Artif Org,18:439-47 (1994); H. Heise, R. Marbach, Effect of data pretreatment onthe noninvasive blood glucose measurement by diffuse reflectance near-IRspectroscopy, SPIE Proc, 2089:114-5 (1994); R. Marbach, T. Koschinsky,F. Gries, H. Heise, Noninvasive glucose assay by near-infrared diffusereflectance spectroscopy of the human inner lip, Appl Spectrosc,47:875-81 (1993) and R. Marbach, H. Heise, Optical diffuse reflectanceaccessory for measurements of skin tissue by near-infrared spectroscopy,Applied Optics 34(4): 610-21 (1995) present results through a diffusereflectance measurement of the oral mucosa in the 1111-1835 nm rangewith an optimized diffuse reflectance accessory. In vivo experimentswere conducted on single diabetics using glucose tolerance tests and ona population of 133 different subjects. The best standard error ofprediction reported was 43 mg/dl and was obtained from a two-day singleperson oral glucose tolerance test that was evaluated throughcross-validation.

[0010] K. Jagemann, C. Fischbacker, K. Danzer, U. Muller, B. Mertes,Application of near-infrared spectroscopy for noninvasive determinationof blood/tissue glucose using neural network, Z Phys Chem, 191S: 179-190(1995); C. Fischbacker, K. Jagemann, K. Danzer, U. Muller, L.Papenkrodt, J. Schuler, Enhancing calibration models for noninvasivenear-infrared spectroscopic blood glucose determinations, Fresenius JAnal Chem 359:78-82 (1997); K. Danzer, C. Fischbacker, K. Jagemann, K.Reichelt, Near-infrared diffuse reflection spectroscopy for noninvasiveblood-glucose monitoring, LEOS Newsletter 12(2): 9-11 (1998); and U.Muller, B. Mertes, C. Fischbacker, K. Jagemann, K. Danzer, Noninvasiveblood glucose monitoring by means of new infrared spectroscopic methodsfor improving the reliability of the calibration models, Int J ArtifOrgans, 20:285-290 (1997) recorded spectra in diffuse reflectance overthe 800-1350 nm range on the middle finger of the right hand with afiber-optic probe. Each experiment involved a diabetic subject and wasconducted over a single day with perturbation of blood glucose levelsthrough carbohydrate loading. Results, using both partial least squaresregression and radial basis function neural networks were evaluated onsingle subjects over single days through cross-validation. Danzer, etal., supra, report an average root mean square prediction error of 36mg/dl through cross-validation over 31 glucose profiles.

[0011] J. Burmeister, M. Arnold, G. Small, Human noninvasive measurementof glucose using near infrared spectroscopy [abstract], Pittcon, NewOrleans, La. (1998) collected absorbance spectra through a transmissionmeasurement of the tongue in the 1429-2000 nm range. A study of fivediabetic subjects was conducted over a 39-day period with five samplestaken per day. Every fifth sample was used for an independent test setand the standard error of prediction for all subjects was greater than54 mg/dl.

[0012] In T. Blank, T. Ruchti, S. Malin, S. Monfre, The use ofnear-infrared diffuse reflectance for the noninvasive prediction ofblood glucose, IEEE Lasers and Electro-Optics Society Newsletter, 13:5(October 1999), the reported studies demonstrate noninvasive measurementof blood glucose during modified oral glucose tolerance tests over ashort time period. The calibration was customized for the individual andtested over a relatively short time period.

[0013] In all of these studies, limitations were cited that would affectthe acceptance of such a method as a commercial product. Theselimitations included sensitivity, sampling problems, time lag,calibration bias, long-term reproducibility and instrument noise.Fundamentally, however, accurate noninvasive estimation of blood glucoseis presently limited by the available near-infrared technology, thetrace concentration of glucose relative to other constituents and thedynamic nature of the skin and living tissue of the patient (forexample, see O. Khalil, Spectroscopic and clinical aspects ofnoninvasive glucose measurements, Clin Chem, 45:165-77 (1999)). Asreported by S. Malin, T. Ruchti, An Intelligent System for NoninvasiveBlood Analyte Prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001), theentirety of which is hereby incorporated by reference, chemical,structural and physiological variations occur that produce dramatic andnonlinear changes in the optical properties of the tissue sample [see R.Anderson, J. Parrish, The optics of human skin, Journal of InvestigativeDermatology, 7:1, pp.13-19 (1981), W. Cheong, S. Prahl, A. Welch, Areview of the optical properties of biological tissues, IEEE Journal ofQuantum Electronics, 26:12, pp.2166-2185, (December 1990), D. Benaron,D. Ho, Imaging (NIRI) and quantitation (NIRS) in tissue usingtime-resolved spectrophotometry: the impact of statically anddynamically variable optical path lengths, SPIE, 1888, pp.10-21 (1993),J. Conway, K. Norris, C. Bodwell, A new approach for the estimation ofbody composition: infrared interactance, The American Journal ofClinical Nutrition, 40, pp.1123-1140 (December 1984), S. Homma, T.Fukunaga, A. Kagaya, Influence of adipose tissue thickness in nearinfrared spectroscopic signals in the measurement of human muscle,Journal of Biomedical Optics, 1:4, pp.418-424 (October 1996), A. Profio,Light transport in tissue, Applied Optics, 28:12), pp. 2216-2222, (June1989), M. Van Gemert, S. Jacques, H. Sterenborg, W. Star, Skin optics,IEEE Transactions on Biomedical Engineering, 36:12, pp.1146-1154(December 1989), and B. Wilson, S. Jacques, Optical reflectance andtransmittance of tissues: principles and applications, IEEE Journal ofQuantum Electronics, 26:12, pp. 2186-2199].

[0014] The measurement is further complicated by the heterogeneity ofthe sample, the multi-layered structure of the skin and the rapidvariation related to hydration levels, changes in the volume fraction ofblood in the tissue, hormonal stimulation, temperature fluctuations andblood analyte levels. This can be further considered through adiscussion of the scattering properties of skin.

[0015] Tissue Scattering Properties

[0016] Skin Structure

[0017] The structure and composition of skin varies widely amongindividuals as well as between different sites and over time on the sameindividual. Skin consists of a superficial layer known as the stratumcorneum, a stratified cellular epidermis, and an underlying dermis ofconnective tissue. Below the dermis is the subcutaneous fatty layer oradipose tissue. The epidermis, with a thickness of 10-150 μm, togetherwith the stratum corneum provides a barrier to infection and loss ofmoisture, while the dermis is the thick inner layer that providesmechanical strength and elasticity [F. Ebling, The Normal Skin, Textbookof Dermatology, 2^(nd) ed.; A. Rook; D. Wilkinson, F. Ebling, Eds.;Blackwell Scientific, Oxford, pp 4-24 (1972)]. In humans, the thicknessof the dermis ranges from 0.5 mm over the eyelid to 4 mm on the back andaverages approximately 1.2 mm over most of the body [S. Wilson, V.Spence, Phys. Med. Biol., 33:894-897 (1988)].

[0018] In the dermis, water accounts for approximately 70% percent ofthe volume. The next most abundant constituent is collagen, a fibrousprotein comprising 70-75% of the dry weight of the dermis. Elastinfibers, also a protein, are plentiful though they constitute only asmall proportion of the bulk. In addition, the dermis contains a widevariety of structures (e.g., sweat glands, hair follicles and bloodvessels) and other cellular constituents [see F. Ebling, supra].Conversely, the subcutaneous layer (adipose tissue) is by volumeapproximately 10% water and consists primarily of cells rich intriglycerides (fat). The concentration of glucose varies in each layeraccording to the water content, the relative sizes of the fluidcompartments, the distribution of capillaries and the perfusion ofblood. Due to the high concentration of fat, the average concentrationof glucose in subcutaneous tissue is significantly lower than that ofthe dermis.

[0019] Optical Properties of Skin

[0020] When near-infrared light is delivered to the skin, a percentageof it is reflected, while the remainder penetrates into the skin. Theproportion of reflected light, or specular reflectance is typicallybetween 4-7% of the delivered light over the entire spectrum from250-3000 nm (for a perpendicular angle of incidence) [J. Parrish, R.Anderson, F. Urbach, D. Pitts, UV-A: Biologic Effects of UltravioletRadiation with Emphasis on Human Responses to Longwave Ultraviolet, NewYork, Plenum Press (1978)]. The 93-96% of the incident light that entersthe skin is attenuated due to absorption and scattering within the manylayers of the skin. These two processes, combined with orientation ofthe sensors of the spectrometer instrument, determine the tissue volumeirradiated by the source and “sampled” through the collection ofdiffusely reflected light.

[0021] Diffuse reflectance or remittance is defined as that fraction ofincident optical radiation that is returned from a turbid sample.Alternately, diffuse transmittance is the fraction of incident opticalradiation that is transmitted through a turbid sample. Absorption by thevarious skin constituents mentioned above accounts for the spectralextinction of the light within each layer. Scattering is the onlyprocess by which the beam may be returned to contribute to the diffusereflectance of the skin. Scattering also has a strong influence on thelight that is diffusely transmitted through a portion of the skin.

[0022] The scattering in tissues is due to discontinuities in therefractive index on the microscopic level, such as the aqueous-lipidmembrane interfaces between each tissue compartment or the collagenfibrils within the extracellular matrix [B. Wilson, S. Jacques, Opticalreflectance and transmittance of tissues: principles and applications,IEEE Journal of Quantum Electronics, 26:12 (December 1990)]. The spatialdistribution and intensity of scattered light depends upon the size andshape of the particles relative to the wavelength, and upon thedifference in refractive index between the medium and the constituentparticles. The scattering of the dermis is dominated by the scatteringfrom collagen fiber bundles in the 2.8 μm diameter range occupyingtwenty-one percent of the dermal volume, and the refractive indexmismatch is 1.38/1.35 [S. Jacques, Origins of tissue optical propertiesin the UVA, Visible and NIR Regions, Optical Society of America, TopicalMeeting, Orlando Fla. (Mar. 18-22, 1996)]. The spectral characteristicsof diffuse remittance from tissue result from a complex interplay of theintrinsic absorption and scattering properties of the tissue, thedistribution of the heterogeneous scattering components and the geometryof the point(s) of irradiation relative to the point(s) of lightdetection.

[0023] The absorption of light in tissue is primarily due to threefundamental constituents: water, protein and fat. As the mainconstituent, water dominates the near-infrared absorbance above 1100 nmand is observed through pronounced absorbance bands (for example, seeFIG. 3). Protein in its various forms, and in particular collagen, is astrong absorber of light that irradiates the dermis. Near-infrared lightthat penetrates to subcutaneous tissue is absorbed primarily by fat. Inthe absence of scattering, the absorbance of near-infrared light due toa particular analyte, A, can be approximated by Beers Law at eachwavelength by

A=εcl  (1)

[0024] where ε is the analyte specific absorption coefficient, c is theconcentration and/is the pathlength. The overall absorbance at aparticular wavelength is the sum of the individual absorbances of eachparticular analyte given by Beer's Law. The concentration of aparticular analyte, such as glucose, can be determined through amultivariate analysis of the absorbance over a multiplicity ofwavelengths because E is unique for each analyte. However, in tissuecompartments expected to contain glucose, the concentration of glucoseis at least three orders of magnitude less than that of water.Consequently, the signal targeted for detection by reported approachesto near-infrared measurement of glucose (the absorbance due to glucosein the tissue) is expected to be at most three orders of magnitude lessthan other interfering tissue constituents. Therefore, the near-infraredmeasurement of glucose requires a high level of sensitivity over a broadwavelength range, and the application of methods of multivariateanalysis.

[0025] However, the diverse scattering characteristics of the skin(e.g., multiple layers and heterogeneity) cause the light returning froman irradiated sample to vary in a highly nonlinear manner with respectto tissue analytes, in particular, glucose. Simple linear models, suchas the Beer's Law have been reported to be invalid for the dermis [R.Anderson, J. Parrish, The optics of human skin, Journal of InvestigativeDermatology, 77:1, pp. 13-19 (1981).]. Such nonlinear variation is arecognized problem and several reports have disclosed unique methods forcompensating for the nonlinearity of the measurement while providing thenecessary sensitivity [see S. Malin, et al., supra; E. Thomas, R. Rowe,Methods and Apparatus for Tailoring Spectroscopic Calibration Models,U.S. Pat. No. 6,157,041 (Dec. 5, 2000).].

[0026] Dynamic Properties of the Skin

[0027] While knowledge of and utilization of the optical properties ofthe skin, high instrument sensitivity and compensation for inherentnonlinearities are all vital for the application of near-infraredspectroscopy to noninvasive blood analyte measurement, an understandingof biological and chemical mechanisms that lead to time dependentchanges in the optical properties of skin tissue is equally importantand, yet, largely ignored. At a given measurement site, skin tissue isoften assumed to be static except for changes in the target analyte andother absorbing species. However, variations in the physiological stateof tissue profoundly affect the optical properties of tissue layers andcompartments over a relatively short period of time. Such variations areoften dominated by fluid compartment equalization through water shiftsand are related to hydration levels and changes in blood analyte levels.

[0028] Total body water accounts for over 60% of the weight of theaverage person and is distributed between two major compartments: theextracellular fluid (one-third of total body water) and theintracellular fluid (two-thirds of total body water) [see A. Guyton, J.Hall, Textbook of Medical of Physiology, 9^(th) ed., Philadelphia, W. B.Saunders Company (1996)]. The extracellular fluid in turn is dividedinto the interstitial fluid (extravascular) and the blood plasma(intravascular). Water permeable lipid membranes separate thecompartments and water is transferred rapidly between them through theprocess of diffusion, in order to equalize the concentrations of waterand other analytes across the membrane. The net water flux from onecompartment to another constitutes the process of osmosis and the amountof pressure required to prevent osmosis is termed the osmotic pressure.Under static physiological conditions the fluid compartments are atequilibrium. However, during a net fluid gain or loss as a result ofwater intake or loss, all compartments gain or lose water proportionallyand maintain a constant relative volume.

[0029] The primary mechanism for distributing substances contained inblood serum that are needed by the tissues, such as water and glucose,is through the process of diffusion. The invention recognizes thatFick's law of diffusion drives the short-term intra-/extra vascularfluid compartment balance. The movement of water and other analytes fromintravascular to extravascular compartments occurs rapidly as tremendousnumbers of molecules of water and other constituents, in constantthermal motion, diffuse back and forth through the capillary wall. Onaverage, the rate at which water molecules diffuse through the capillarymembrane is about eighty times greater than the rate at which the plasmaitself flows linearly along the capillary. In the Fick's Law expression,the actual diffusion flux, I_(OA), is proportional to the concentrationgradient, dc/dx between the two compartments and the diffusivity of themolecule, D_(A) according to the equation $\begin{matrix}{I_{OA} = {{- D_{A}}\frac{- {c}}{{x}\quad}.}} & (2)\end{matrix}$

[0030] Short-term increases (or decreases) in blood glucoseconcentrations lead to an increase (or decrease) in blood osmolality(number of molecules per unit mass of water). Fluid is rapidlyre-distributed accordingly and results in a change in the waterconcentration of each body compartment. For example, the osmotic effectof hyperglycemia is a movement of extravascular water to theintravascular space. Conversely, a decrease in blood glucoseconcentration leads to a movement of water to extravascular space fromthe intravascular compartment.

[0031] Because the cell membrane is relatively impermeable to mostsolutes but highly permeable to water, whenever there is a higherconcentration of a solute on one side of the cell membrane, waterdiffuses across the membrane toward the region of higher soluteconcentration. Large osmotic pressures can develop across the cellmembrane with relatively small changes in the concentration of solutesin the extracellular fluid. As a result, relatively small changes inconcentration of impermeable solutes in the extracellular fluid, such asglucose, can cause tremendous changes in cell volume.

[0032] Long-term fluid compartment balances are influenced by fluidintake, exercise, diet, drug therapy and other physiological factors.The ancillary calibration of glucose to fluid compartment shifts ispossible over short-term periods. The calibration of glucose to fluidshifts over longer periods of time requires a bias correction of theanalytical signal and the associated blood glucose to compensate for thesources of long-term fluid compartment shifts. It is noted that Fick'sLaw (equation 2) relates the flux in water concentration to the changein glucose concentration. Thus, this measurement based on firstprinciples only permits the determination of the relative movement ofglucose. Bias correction of both the spectroscopic water signal and theassociated glucose concentration are required because the initial waterconcentration is not strictly a function of the associated glucoseconcentration. Accordingly, without more, it is only feasible to predictrelative movement of glucose. Generating an absolute glucose value wouldrequire using a paired glucose/water measurement to adjust the timedependent bias in the ancillary fluid shift signal.

[0033] The Problem

[0034] Re-distribution of water between various tissue compartmentsalters the optical properties of the tissue through changes in the waterconcentration, the concentration of other analytes, the refractiveindices of various layers, the thickness of tissue layers and the sizeand distribution of scattering centers. Therefore, the opticalproperties of the tissue sample are modified in a highly nonlinear andprofound manner. In addition, the actual tissue volume sampled (and theeffective or average pathlength of light) is varied. Consequently, thespectral measurement varies in a complex manner that is incompatiblewith current modes of near-infrared detection of glucose. For example,changes in blood glucose concentration will result in water compartmentshifts to compensate for the increase or decrease in intravascularosmolality. A change in the distribution of water will lead to a rapidchange in the optical properties of the tissue that is correlated to achange in the absorption of glucose.

[0035] Several methods are reported to compensate in some part for thedynamic variation of the tissue. For example, several reported methodsof noninvasive glucose measurement develop calibration models that arespecific to an individual over a short period of time [see Robinson, etal., supra; Burmeister et al., supra; Blank et al., supra; K. Hazen,Glucose determination in biological matrices using near-infraredspectroscopy, Doctoral Dissertation, University of Iowa (August, 1995);and J. Burmeister, In vitro model for human noninvasive blood glucosemeasurements, Doctoral Dissertation, University of Iowa (December 1997].This approach avoids modeling the differences between patients andtherefore cannot be generalized to more individuals. However, thecalibration models have not been tested over long time periods and nomeans of compensating for variation related to the dynamic water shiftsof fluid compartments is reported.

[0036] Malin and Ruchti, supra report a method for compensating forvariation related to the structure and state of the tissue through anintelligent pattern recognition system capable of determiningcalibration models that are most appropriate for the patient at the timeof measurement. The calibration models are developed from the spectralabsorbance of a representative population of patients that have beensegregated into groups. The groups or classes are defined on the basisof structural and state similarity such that the variation within aclass is small compared to the variation between classes. Classificationoccurs through extracted features of the tissue absorbance spectrumrelated to the current patient state and structure. However, thedescribed invention does not use features for directly compensating forphysiological changes in the tissue. Further, the direct use of featuresrepresenting the physiological state of the subject (or subject'smeasurement site) for noninvasive measurement of glucose was notdescribed.

[0037] E. Thomas, R. Rowe, Methods and Apparatus for TailoringSpectroscopic Calibration Models, U.S. Pat. No. 6,157,041 (Dec. 5, 2000)identifies a method for reducing intra-subject variation through theprocess of mean-centering both the direct and indirect measurements.However, this does not address the key problem related to short-termphysiological and chemical changes related to the dynamic nature of thetissue.

[0038] No reported method provides a method and apparatus for detectingfeatures that reflect changes in the optical properties of tissuerelated to physiological properties of the tissue such as the shiftingof water between fluid compartments. Second, no reported method utilizesfeatures that reflect the dynamic nature of the tissue to detectconditions unsuitable for near-infrared measurement of blood glucose.Third, no method exists to use these features to compensate glucosemeasurements for bias caused by physiological changes. Finally, noreported method utilizes fluid compartment shifts as reflected inspectral features related to the optical properties of tissue toindirectly measure glucose. As a result, noninvasive measurement ofglucose is limited by the dynamic nature of tissue related to thetissue's physiological response to various conditions and there-distribution of water among tissue fluid compartments.

[0039] In view of the problems left unsolved by the prior art, thereexists a need for a method and apparatus to first detect changes in theoptical properties of the tissue due to the changing physiology of thesubject, specifically changes related to water shifts between tissuecompartments. Second, use of these features to determine conditionsunsuitable for glucose measurement through near-infrared spectroscopywould be a useful advancement. Finally, it would be a significantadvancement to determine a means for either using the features tocompensate for the changing optical properties of the tissue oralternately, utilizing the features to measure glucose.

SUMMARY OF THE INVENTION

[0040] Changes in the distribution of water among tissue compartmentsand other physiological conditions lead to complex alterations in themeasured absorbance spectrum of skin and reflect a modification in theeffective pathlength of light. These dynamic changes lead to a biasednoninvasive glucose measurement and have limited the state of thetechnology. This disclosure provides a method and apparatus forutilizing the optical properties of tissues as reflected in keyspectroscopic features to improve the accuracy and precision of thenoninvasive measurement of glucose through near-infrared spectroscopy.Specifically, a method is provided for detecting changes in the opticalproperties of tissue through the identification of key features that areresponsive to and reflect physiological variations. Secondly, a processis given for detecting conditions that are not conducive to noninvasivemeasurement of glucose. Third, a means is provided for compensatingnoninvasive glucose measurements that are biased by physiologicalchanges in the tissue. Fourth, a method is disclosed for measuringglucose indirectly on the basis of the natural physiological response ofthe tissue to the concentration of glucose. Finally, the apparatusrequired to make such a measurement is detailed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0041]FIG. 1 provides a block diagram of a system for measuring glucosenoninvasively through near-infrared spectroscopy according to theinvention;

[0042]FIG. 2 provides a block diagram of a spectrometer from the systemof FIG. 1 according to the invention;

[0043]FIG. 3 shows a typical absorbance spectrum measurement from theforearm of a human subject;

[0044]FIG. 4 shows a normalized second derivative of an absorbancespectrum versus wavelength;

[0045]FIG. 5 shows the second derivative of an absorbance spectrum inthe first overtone with features identified according to the invention;

[0046]FIG. 6 shows the second derivative of the absorbance spectrum ofFIG. 5 in the vicinity of the 1910 nm water band;

[0047]FIG. 7 shows the second derivative of the absorbance spectrum ofFIG. 54 in the vicinity of the 1450 nm water band;

[0048]FIG. 8 shows the second derivative of the absorbance spectrum ofFIG. 5 in the second overtone region with exemplar features according tothe invention;

[0049]FIG. 9 shows the second derivative of the absorbance spectrum ofFIG. 5 in the combination band with key features marked according to theinvention; and

[0050]FIG. 10 shows a plot of a normalized fat band versus glucoseconcentration according to the invention.

DETAILED DESCRIPTION

[0051] A major difficulty in noninvasively measuring glucose throughnear-infrared spectroscopy arises from the fact that glucose is presentin very small amounts (2-20 mM). Calibrating a sensor to this smallglucose signal requires extraction of the signal from a massivebackground due to variations in tissue characteristics and hydration.These background variations result in changes in the optical propertiesof the sampled tissue leading to confounding effects due to theresulting pathlength changes causing large uncertainties in theextracted signal. Long-term variations (over a few days) in tissuecharacteristics are especially bothersome since their effects could belarge enough to swamp the small glucose signal. While the glucose signalis small, changes in blood glucose triggers physiological responses thatare large. A major physiological response that can be detectedspectrally is the fluid shift that occurs due to changes in bloodglucose, which causes water to move in and out of the vascular andcellular compartments. This redistribution of water causes changes inthe scattering and absorption properties of skin leading to significantchanges in the observed spectra of skin. This change in spectra due tochanges in fluid distribution as a response to changes in blood glucosehas proven extremely useful for building robust calibration models forglucose measurement.

[0052] More specifically, changes in blood glucose leads to changes inthe distribution and content of water in skin tissue. This variationcauses a change in the refractive index (and thus the scatteringcoefficient) and a change in the absorption coefficient of tissue. As aresult, the depth to which light penetrates the tissue is changed. Inthe case of a diffuse reflectance measurement, the changes in theabsorption and scattering properties affect the amount of light from acertain depth in the tissue that reaches the detector. For example,changes in the water content in the dermis will dictate the amount oflight reaching the detector that has probed the subcutaneous tissue andthereby changes the total amount of light that is absorbed by fat. Inother words, changes in the fluid distribution will change the magnitudeof the fat absorbance signal detected. The invention described hereinbelow is based upon this discovery.

[0053] In recognition of the above discovery, the invention provides amethod for detecting changes in the optical properties of tissue relatedto physiological changes, such as the water distribution among variouscompartments, for determining conditions that are not conducive tononinvasive measurement of glucose through near-infrared spectroscopy,and for:

[0054] correcting the glucose measurement on the basis of detectedchanges in tissue optical properties; or

[0055] measuring glucose indirectly on the basis of features reflectingthe detected optical properties.

[0056] An apparatus for detecting and correcting fluid compartmentchanges and indirectly measuring glucose includes, but is not limitedto:

[0057] a spectrometer system;

[0058] a patient interface module; and

[0059] an analyzer.

[0060] The spectrometer system detects near-infrared light within aspecified range that is diffusely transmitted or reflected from thetargeted tissue, and the analyzer measures glucose through dataprocessing operations and the application of a model. Fundamental to thesystem is the patient interface module, which precisely couples theapparatus to the tissue measurement site physically and optically, withminimal disturbance. In addition, a means, such as an optical couplingmedium, is provided for preparing the sample site for spectroscopicmeasurement prior to contact with the device for the purpose of reducingspecular reflectance and the effects of skin temperature and skinhydration changes.

[0061] An overview of the system is shown in FIG. 1 and generallyconsists of two elements, a spectrometer 101 or instrument and ananalyzer 208 embodying the process for obtaining the glucosemeasurement. The spectrometer measures the near-infrared spectrum of asubject's tissue. The analyzer processes the spectral measurement,extracts features relevant to outlier detection and glucose measurementand applies a model to the processed spectral measurement and/or theextracted features to obtain a glucose measurement. A detaileddescription of the spectrometer by system and the components of theanalyzer follow.

[0062] Spectrometer System

[0063] The spectroscopic measurement system 101 consists of a source ofnear-infrared radiation 200, a wavelength selection system 201, aninterface to the patient, a means for directing the near-infraredradiation to the skin 203 and a means for directing radiation reflectedor transmitted from the skin 205, a means for detecting near-infraredradiation that is reflected or transmitted from the skin 206, a meansfor analyzing the detected near-infrared radiation 208 and a means fordisplaying the measured analyte, property or constituent 209.

[0064] In an alternate arrangement, the wavelength selection 201 canoccur between the subject interface 203 and the detector optics 205.

[0065] The source 200 radiates near-infrared energy in the wavelengthrange 700-2500 nm and may consist of, for example, an array of LED's ora halogen lamp. One or more bandpass filters are (optionally) requiredto minimize the effect of wavelengths from outside of the spectral rangeof interest, but which are still emitted by the source of near-infraredenergy. For example, halogen lamps, while having peak energy atapproximately 1600 nm, still give off electromagnetic radiation above2500 nm. This has detrimental effects on the detection of glucose sincewavelengths above 2500 nm have deleterious effects at the measurementsite due to heating of the tissue and its respective components.

[0066] The method of wavelength separation 201 before and or afterillumination of the skin can be performed through the use of adispersive element (e.g., a plane or concave, ruled or holographicgrating), an interferometer, or successive illumination of the elementsof an LED array without an additional dispersive element. Due to changesin performance of these wavelength separation methods caused by changesin the environment, it is necessary to correct for these changes byscanning a reference wavelength standard 202, for example a polystyrenestandard, either immediately before or after the interrogation of thetissue. In interferometer-based systems, this is done simultaneouslywith the interrogation of the tissue.

[0067] The sensing element(s) 206 are detectors that are responsive tothe targeted wavelengths and may constitute either an array or a singleelement. In the case of linear diode arrays (or photodiode arrays), whentwo or more different detector materials are required to cover thewavelength region of interest, it is preferable that the materialjunction(s) occurs at a wavelength not required for the measurement. Forexample, in the case of InGaAs and extended InGaAs detectors, thejunction typically occurs at 1750 nm for the purpose of reducing thecost of the array due to the high cost of extended InGaAs. However, thiswavelength region occurs in the middle of the absorptions associatedwith fat, protein and glucose; thus, it is much preferable for thejunction to occur at approximately 1480 nm±20 nm. In addition, it ispreferable that the electronics used to sense the individual elements ofthe array have their junction occurring at the same wavelength.

[0068] The tissue sample interface includes a subject 204 interfacemodule 203 by which near-infrared radiation is directed to and from 205the tissue, either directly or through a light pipe, fiber optics, alens system or a light directing mirror system. The area of the tissuesurface to be irradiated and the area from which the returningnear-infrared radiation is detected fare different, being separated by adefined distance and selected in order to target a tissue volume optimalto measurement of the property of interest. The specularly reflectedradiation from the irradiated site is of such a magnitude that it wouldgreatly interfere with detection of the returned radiation. Thus, inoffsetting the detection site from the irradiation site by apredetermined amount, it is possible to sample a volume of tissue thatis a subset of the manifold of tissue that has affected the light thatis being detected, while avoiding interference from specularly reflectedlight. In the case of a larger, tabletop or desktop instrument, thepatient interface module further includes an elbow rest, a wrist rest,and a guide to assist in interfacing the illumination mechanism ofchoice and the tissue of interest. In the case of a smaller handheldunit, the patient interface module includes a guide or positioningmechanism to assist in interfacing the tissue of interest. Generally, asdescribed above, an optical coupling fluid is placed between theillumination mechanism and the tissue of interest to minimize specularreflectance from the surface of the skin. Portions of the aforementionedpatient interface module are described in U.S. patent application Ser.No. 09/563,782 and PCT Application No. US01/29232, the contents of bothof which are hereby incorporated by reference in their entirety.

[0069] The collected near-infrared radiation is converted to a voltageand sampled through an analog-to-digital 207 converter for analysis on amicroprocessor-based system 0.208 and the result of such analysisdisplayed 209.

[0070] The sample site, the surface or point on the subject themeasurement probe comes into contact with, includes the specific tissueirradiated by the spectrometer system. The ideal qualities of the samplesite include homogeneity, immutability and accessibility to the targetanalyte. While several measurement sites can be used, including theabdomen, thigh, hand (palm or back of the hand), ear lobe or finger, inthe preferred embodiment, the volar part of the forearm is used. Inaddition, while the measurement can be made in either diffusereflectance or diffuse transmittance mode, the preferred method isdiffuse reflectance. The scanning of the tissue can be donecontinuously, in the case of an area not subject to pulsation effects,or the scanning can be done intermittently between pulses.

[0071] Spectral Measurement

[0072] The spectrometer system provides a spectral measurement 104 or“spectrum” to the analyzer 208 for determination or measurement of theconcentration of glucose. The spectrum is denoted by the vector m∈

^(1×N) of absorbance values pertaining to a set of N wavelengths λ∈

^(N) that span the near-infrared portion (700-2500 nm) of the spectrum.In the preferred embodiment, the measurement process and absorbancecalculation is as follows: the measured intensity of light from thetissue, I∈

^(1×N), and the light intensity measured from a non-absorbing referencematerial, I_(o)∈

^(1×N), are used to determine m according to $\begin{matrix}{m = {{- \log_{10}}\frac{- I}{I_{o}}}} & (3)\end{matrix}$

[0073] where m is the reflectance spectrum of the skin and is analogousto an absorbance spectrum containing quantitative information that isbased on the known interaction of the incident light with components ofthe body tissue. A plot of m versus λ is shown in FIG. 3, and consistsof absorption bands primarily due to water, fat and protein. Moreparticularly, however, the measurement can consist of a specific set ofwavelengths in the near infrared region that have been optimized for theextraction of features and for the measurement requirements. Forexample, the measurement of glucose is optimally performed in thewavelength range 1100-1935 nm, or a selected subset thereof.

[0074] Alternatively, the spectral measurement can be determinedaccording to $\begin{matrix}{m = {{- \log_{10}}\frac{- I}{I_{r}}}} & (4)\end{matrix}$

[0075] where I_(r)∈

^(1×N) is a representation of the measured tissue intensity at somepoint in time prior to collection of I and can be determined from asingle tissue intensity spectrum or from the mean or a robust estimateof the mean (e.g., the trimmed mean) of several tissue intensityspectra. In another embodiment, the measurement m, can be defined as themeasured intensity, I. Finally, m may consist of either a singlespectrum collected with an instrument or a combination of several(optimally) selected spectra collected over a defined measurement periodand averaged. Methods for selecting the spectra, used to produce thelowest noise measurement, include similarity or distance measures (i.e.,select the most similar) and clustering operations.

[0076] Preprocessing and Feature Extraction

[0077] Feature extraction 106 is any mathematical transformation thatenhances a quality or aspect of the sample measurement forinterpretation [R. Duda, P. Hart, Pattern Classification and SceneAnalysis, John Wiley and Sons, New York (1973)]. The general purpose offeature extraction is to concisely represent or enhance the chemicalconcentration, structural properties and physiological state of thetissue measurement site. In the invention, a set of features isdeveloped that represents or reflects the optical properties of thetissue based on:

[0078] identification of distinct absorption bands that change invarious ways with respect to changes in pathlength; and

[0079] the scattering and absorption properties (or coefficients) of themeasurement site.

[0080] Subsequently, the features are then applied either to identifyconditions unsuitable for glucose measurement or to perform an actualmeasurement of glucose. For example, a resolved estimate of themagnitude of the fat band absorbance can be used to infer specificinformation about the dermis. Although fat is absent from the dermis,near infrared radiation must propagate through the dermis to penetrateinto the adipose tissue beneath. Thus, physiological changes, and thecorresponding changes in the optical properties of the dermis, influencethe magnitude of the fat band absorbance.

[0081] Thus, as water concentration in the dermis increases, themagnitude of the fat band naturally decreases and vice versa.

[0082] Several types of features are determined and used in theinvention for:

[0083] outlier detection 107;

[0084] compensation for changes in the optical properties of tissue 102;and

[0085] glucose measurement 109.

[0086] Given the spectral measurement, m, or a spectral measurementpre-processed 105 by means of a filtering operation, first or secondderivative calculation [A. Savitzky, M. Golay, Smoothing andDifferentiation of Data by Simplified Least Squares Procedures, Anal.Chem., 36: 8, pp. 1627-1639 (1964)] or scatter correction:

[0087] “simple” features are the values of the spectral measurement orthe processed spectral measurement at the critical points (the points atwhich the slope is zero);

[0088] additional (derived) features are determined from the basefeatures through mathematical transformation such as addition,subtraction, division and multiplication; and

[0089] abstract features are derived through linear and nonlineartransformations of the pre-processed spectrum.

[0090] While simple and derived features generally have a physicalinterpretation, such as the magnitude of the fat absorbance, the set ofabstract features do not necessarily have a specific interpretationrelated to the physical system. For example, the scores of a principalcomponent analysis are used as features although their physicalinterpretation is not always known. The utility of the principalcomponent analysis is related to the nature of the tissue absorbancespectrum. The most significant variation in the tissue spectralabsorbance is not caused by the absorption of glucose but is related tothe state, structure and composition of the measurement site. Thisvariation is modeled by the primary principal components. Therefore, theleading principal components tend to represent variation related to thestructural properties and physiological state of the tissue measurementsite and consequently reflect the optical properties of tissue.

[0091] In the preferred embodiment, the features are determined from thesecond derivative of the absorbance spectrum shown in FIG. 4. Eachcritical point is identified according to its wavelength. The value ofthe second derivative spectrum at each critical point is used as afeature to represent a key property of the tissue sample associated withthe measurement spectrum. In FIGS. 5 through 9, many key features areidentified as exemplary measurements. These include:

[0092] Normalization points (n) 1-8 near 1665, 1708, 1746, 1868, 1380,1133, 2020 and 2232 nm respectively;

[0093] Fat bands points (f) 1-4 near 1727, 1765, 1214, 1165 nm;

[0094] Protein band points (p) 1-9 near 1687, 1715, 1190, 2050, 2150,2175, 2275, 2292, and 2355 nm; and

[0095] Water band points (w) 2-6 near 1789, 1896, 1410, 1465 and 1150nm.

[0096] Normalization points, n1-n8, are generally used to determinederived features and points designated as “fat” (f1-f4), “protein” p1-p9and “water” w2-w6 are generally located in the vicinity of an absorptionband due to fat, protein or water respectively. Due to the bandwidth(lower resolution) of the second derivative spectrum, several of thebands associated with one constituent include absorbance due to anotherand a few of the critical points are associated with a constituentbecause their location is in the vicinity of the respective constituent.In addition, the wavelengths are reported for the features shown in theexample second derivative spectrum and can change substantially as aresult of variation in the reduced scattering coefficient and the innerfilter effect related to the multiple layers of the skin.

[0097] Additional features have been derived and are noted on the plots.For example, d1=n₁₆₆₅-P₁₆₈₇, d2=n₁₆₆₅−f₁₇₂₇, d3=n₁₆₆₅−f₁₇₆₅,d4=n1665−w₁₇₈₉, d5=n₁₈₆₈−w₁₄₁₀, d6=n₁₃₈₀−w₁₄₆₅ and d7=n₁₃₈₀−w₁₁₅₀, wherethe notation p_(λ), w_(λ), f_(λ), and n_(λ) designate the protein,water, fat or normalization points designated previously that are closeto the wavelength λ. Additional derived features that are used foroutlier detection and measurement include d2/d1.

[0098] While specific examples of features have been provided in thiscontext, one skilled in the art will recognize that many useful featureshave not been listed that can be derived from the absorbance spectrum,the first derivative spectrum or a preprocessed absorbance spectrum.Additionally, a principal components analysis provides additionalabstract features that are useful for tissue transient identification,outlier analysis and analyte measurement. In certain instances, theentire spectrum, after suitable preprocessing, is passed to themeasurement module in which a calibration is applied to estimate orpredict the concentration of blood glucose.

[0099] Finally, features related to the absorption of glucose areextracted through preprocessing, wavelength selection and abstractfeature selection. In the preferred embodiment preprocessing includes onor more steps of filtering, differentiation, scatter correction andnormalization. Wavelength selection limits the spectrum to regionspertaining specifically to glucose including 1450-1700 nm, 1700-1900 nm,2050-2200 nm, and 2250-2400 nm.

[0100] Tissue Template (102)

[0101] A background subtraction step follows the preprocessing stepsdefined above through the determination of the difference between theestimated spectral background or tissue template 102 and x through

z=x−(cx _(t) +d)  (5)

[0102] where x is the preprocessed spectrum or the selected set offeatures, x_(t) is the estimated background or tissue templateassociated with the measurement period, and c and d are slope andintercept adjustments to the tissue template. During each measurementperiod, defined by a measurement position on the tissue and a level ofphysiological stability of the measurement site, the tissue template isdetermined through one or more spectral measurements and a dataselection criterion, for example, by selecting only spectralmeasurements that resemble each other closely and averaging them. In thepreferred embodiment, x_(t) includes features extracted from a(spectral) measurement collected on tissue at the beginning of themeasurement period. This process is referred to as “re-calibration” andinvolves both the collection of one or more spectral measurements thatare processed to form a tissue template as well as an associated set ofreference glucose values. The glucose values are combined, according tothe same strategy as that used to create the tissue template to form ameasurement bias adjustment 103, described in greater detail below. Themeasurement period is defined as a time period during which the state ofthe tissue sample is uniform (optical properties within a preset bound)and the tissue measurement site is constant. However, the tissuetemplate can also be any set of features from a given patient orcalibration set that future spectral measurements will be compared with.In this latter embodiment, the variables c and d are determined througha least-squares fit (to minimize the Euclidean norm of z) of the tissuetemplate over a particular wavelength range to the measured spectrum.

[0103] Detection of Tissue States

[0104] As discussed previously, changes in the distribution of water inthe various compartments lead to changes in the optical properties thatare reflected by changes in the spectral features. Therefore, conditionsthat are detrimental to spectroscopic glucose measurement can bedetected by monitoring the selected features and ensuring that theirvariation over a given measurement period does not exceed that of thecalibration set or some other previously established limit. For example,the variation of d2 (n₁₆₆₅−f₁₇₂₇), the magnitude of the normalized fatband, has been used to determine hydration state of the dermis. If themagnitude of d2, compared to the tissue template, exceeds the totalvariation or the range established by samples selected to calculate thecalibration model, an error is indicated. Similarly, the normalizedprotein band (d1=n₁₆₆₅−p₁₆₈₇), various normalized water bands(d4=n₁₆₆₅−w₁₇₈₉, d5=n₁₈₆₈−w₁₄₁₀, d6=n₁₃₈₀−w₁₄₆₅ and d7=n₁₃₈₀−w₁₁₅₀) andthe ratio d1/d2 are used to detect outliers 107 and conditions that arenot conducive to glucose measurement. This method can be applied to anyof the identified features listed previously.

[0105] Measurement (109)

[0106] The measurement of glucose is accomplished through theapplication of a calibration model 108 to the processed spectralmeasurement and/or the extracted features. The model is determined froma calibration set of exemplary paired data points each consisting of apre-processed spectral measurement (x) and an associated referenceglucose value (y) determined from an analysis of a sample of blood orinterstitial fluid. Alternately, the reference glucose measurements canbe determined from a blood draw at the fingertip or site of the spectralmeasurement. Finally, the reference glucose measurements can bedetermined from interstitial glucose concentrations taken at or near thesite of spectroscopic measurement or alternate representative site, forexample the forearm.

[0107] According to this process, blood, serum, plasma or interstitialdraws are taken from a tissue site that is either near the sensor samplesite or has been designed/determined to reflect the sample site. Forexample, when non-invasive near-infrared measurements are taken forcalibration on the forearm, it is possible in some individuals tocollect a capillary blood draw from the same forearm or the oppositeforearm. Alternately, rather than using blood draws, it is beneficial insome instances to use interstitial glucose values rather than capillaryglucose values.

[0108] The calibration set is based on one or more subjects andgenerally contains glucose concentrations that span the expected rangeof glucose variation and that include spectral variation representativeof that likely to be encountered in future spectral measurements. Thecalibration model 108 includes an equation, a set of parameters andcorresponding computer code that is implemented to measure the subject'sglucose level on the basis of the preprocessed spectral measurement. Inthe preferred embodiment, the preprocessing and feature extraction,together with the model, efficiently extract the net analyte signal ofglucose where net analyte signal is the portion of the spectral signalrelated to the target analyte that is orthogonal to the interference [A.Lorber, K. Faber, B. Kowalski, Net Analyte Signal Calculation inMultivariate Calibration, Anal. Chem, 69, pp. 1620-1626 (1997)]. The netanalyte signal is then scaled and bias corrected 103 to match thedesired units of glucose measurement (e.g. mg/dL).

[0109] Several embodiments of the invention are disclosed under twocategories. In the first measurement category the extracted features aresupplemental and are applied to compensate another model for variationin the optical properties related to a change in the effectivepathlength of detected light and sample tissue volume but which changesare unrelated to absorption due to glucose. This is accomplished byusing the absorption features that reflect the changes in tissue opticalproperties related to a water shift between compartments (or otherphysiological transient condition) to supplement a calibration that isbased on the near-infrared absorption of glucose.

[0110] In the second measurement category, the extracted featuresrelated to the physiological and chemical response of the body areprimary and used to indirectly measure the subject's glucose level. Themethod is based on the natural response to changes in blood glucose,which result in the alteration of fluid distribution in theinterstitial, vascular and cellular compartments. Such alteration offluid distribution causes changes in the scattering and absorptionproperties of tissue that are detectable through near-infraredspectroscopy and which serve as a basis for an indirect blood glucosemeasurement. The near-infrared signal reflects the changes in thescattering properties from different layers in skin that coincide withchanges in glucose concentration. Thus, the changes in fluiddistribution lead to changes in the apparent absorption of keyconstituents, such as fat, protein and water that provide a signal thatis substantially higher than that of glucose and can be used as markersfor measuring glucose noninvasively. However, long-term fluidcompartment balances are influenced by fluid intake, exercise, diet,drug therapy and other physiological factors.

[0111] The “ancillary” calibration of glucose to fluid compartmentshifts is possible over short term periods while the calibration ofglucose to fluid shifts over longer periods of time requires a biascorrection of the analytical signal and the associated blood glucose tocompensate for the sources of long term fluid compartment shifts (it isnoted that Fick's Law in Equation 2 relates the flux in waterconcentration to the change in glucose concentration). Thus, thismeasurement only permits the determination of the movement of glucoserelative to an initial point in time; and bias correction of both thespectroscopic water signal and the associated glucose concentration tothis point is required because the initial water concentration is notstrictly a function of the associated glucose concentration. Therefore,in this embodiment of the invention, there is provided an apparatus andmethod that measures the change in the optical properties of tissue asreflected in key constituents and a method for determining the glucoseconcentration on the basis of these properties.

[0112] Supplemental measurement of glucose through spectral features isperformed either through the classification system previously disclosed[Malin et al., supra] or by supplementing the glucose measurement modelwith the selected features through the general equation

ŷ=f(x _(p) ,z)+b  (6)

[0113] where ŷ is the estimated glucose concentration, x_(p)∈

^(N) is a processed spectral measurement, z∈

^(M) is the set of features representative of the physiological state oroptical properties of the tissue, f.

^(N,M)→

¹ is a model used to measure glucose on the basis of the preprocessedspectrum and extracted features, and b is a baseline adjustment for theglucose measurement associated with both the tissue template andcalibration model. The model, f(?), is determined through a calibrationset including spectral measurements, extracted features and referenceglucose values (from blood or interstitial measurements). The method fordesigning the structure of f(?) is through the process of system ofidentification [L. Ljung, Systems Identification: Theory for the User,2d.ed., Prentice Hall (1999)]. The model parameters are calculated usingknown methods including multivariate regression or weighted multivariateregression [N. Draper, H. Smith, Applied Regression Analysis, 2d.ed.,John Wiley and Sons, New York (1981)], principal component regression[H. Martens, T. Naes, Multivariate Calibration, John Wiley and Sons, NewYork (1989)], partial least squares regression [P. Geladi, B. Kowalski,Partial least-squares regression: a tutorial, Analytica Chimica Acta,185, pp.1-17, (1986)], or artificial neural networks [S. Haykin, NeuralNetworks: A Comprehensive Foundation, Prentice Hall, Upper Saddle RiverN.J. (1994)].

[0114] In the case in which x_(p) and z are independent, the generalequation can be reduced to

ŷ=f(x _(p))−(m _(s) g(z)+m _(i))+b  (7)

[0115] where f:

^(N)→

¹ is a model used to measure glucose in the absence of physiological orother tissue variation, g:

^(M)→

¹ is a model used to map the features to a variable correlated to theerror in glucose measurement caused by a change in the opticalproperties of the tissue, and m_(s) and m_(i) are slope and interceptsused to convert g(z) to the correct units. In this case, it is possibleto determine f(?) and g(?) separately through an experimental design.First, f (?) is found through an experiment in which the tissue opticalproperties are stable or constant while the glucose is manipulated.Second, the optical properties of tissue are allowed to fluctuate andg(?), m_(s) and m_(i) are determined on the basis of the error inglucose measurement where the target value for g(?) is given by

r=y−f(x _(p))−b  (8)

[0116] where y is the reference glucose concentration. In the thirdembodiment, when f (?) and g(?) are determined to be linear over therange of measurement, equation #8 reduces to

ŷ=x _(p) F−(m _(s) zG+m _(i))+b  (9)

[0117] where F∈

^(N×1) and G∈

^(M×1). In this embodiment, F and G are determined separately asdescribed above using linear methods of calibration. This finalrealization of the supplemental use of features for glucose measurementis the preferred method.

[0118] In the second category of measurement the extracted features areused to indirectly measure glucose through

ŷ=(m _(s) g(z)+m _(i))+b  (10)

[0119] where g:

^(M)→

¹ is a model used to map the features to a variable correlated to thereference glucose level and m_(s) and m_(i) are slope and interceptsused to convert g(z) to the correct units. The method for determiningg(?) is through an exemplary set (calibration set) of spectralmeasurements, extracted features and reference glucose concentrations(from blood or interstitial measurements). A sub-set of features isselected based on their combined correlation to the reference glucoseconcentration. While a priori knowledge and trial-and-error can beemployed for variable selection, standard methods also exist forvariable selection including stepwise regression [Draper, et al., supra]random search techniques, genetic algorithms [D. Goldberg, GeneticAlgorithm in Search. Optimization and Machine Learning, Addison WesleyPublishing Company (1989)] or evolutionary programming [D. Fogel, AnIntroduction to Simulated Evolutionary Optimization, IEEE Trans. OnNeural Networks, 5:1 (January 1994)]. The model, g(?), is determinedthrough standard methods of linear or nonlinear calibration. In thelinear case,

ŷ=(m _(s) zG+m _(i))+b,  (11)

[0120] where G∈

^(M×1).

[0121] In the preferred embodiment of the invention, the features, z,are selected to include at least the normalized second derivative fatband (d2) or the normalized second derivative protein band (d1). Theparameters of the model (m_(s), m_(i) and G) are determined throughmultivariate regression, weighted multivariate regression or locallyweighted regression. For example, a calibration set was collected on aparticular subject whose glucose concentration spanned the range 70-350mg/dl. A plot of the normalized fat band, d2, versus glucoseconcentration is given in FIG. 10. The high degree of correlationbetween the feature and reference glucose concentration indicates thatglucose measurement is feasible through this extracted feature. A simplelinear regression is performed to determine the model parameters of theequation above.

[0122] However, the invention is not limited to the normalized fat andprotein bands. A similar method has been developed using the waterabsorbance peaks and normalized water absorbance peaks (d4=n₁₆₆₅−w₁₇₈₉,d5=n ₁₈₆₈−w₁₄₁₀, d6=n₁₃₈₀−w₁₄₆₅ and d7=n₁₃₈₀−w₁₁₅₀). Also the wavelengthchosen for normalization is not restricted to 1665 nm. In fact, amultiplicity of models exists for various subjects and categories ofsubjects depending on the optical properties of their respective tissuesample, baseline level of perfusion and physiological response tochanges in glucose concentration. Thus, an alternative embodimentconsists of using a combination of features related to all of the majortypes of absorption bands. For example, the normalized second derivativefat band and two normalized second derivative water bands were selected.Multiple regression of these variables against glucose was thenperformed using a model which could be but not restricted to,

ŷ=g ₁ z ₁ +g ₂ z ₂ +g ₃ z ₃ +g ₄ +b  (12)

[0123] where z₁, z₂ and z₃ are the normalized second derivative valuesof the fat band and two water bands respectively. This equation can thenbe used to measure glucose values from spectra taken in the future afterpreprocessing and feature extraction.

[0124] In an alternate embodiment, abstract features that reflect thechanges in the optical properties of skin tissue, such as the scoresfrom a principal components analysis, can be used as the independentvariables for noninvasive calibration and measurement of glucose. Inthis embodiment, the spectral measurement, m, is preprocessed and isfollowed by wavelength selection to create the preprocessed vector, x. Aspectral decomposition is performed according to

z=xP  (13)

[0125] where x∈

^(1×N) is the preprocessed spectrum, N refers to the number ofwavelengths selected for calibration, P∈

^(1×M) is the set of M eigenvectors or loadings obtained from aprincipal components analysis of the calibration set, and z∈

^(1×M) is the set of abstract features or scores used to develop acalibration model and measure glucose through Equation (14) below, orthrough the application of a nonlinear calibration model. As describedabove, the calibration model can be determined through multivariateregression, weighted multivariate regression, locally weightedregression or other standard approach. While principal componentregression has been described as the method for spectral decomposition,partial least squares regression can also be applied.

[0126] When abstract feature extraction is involved, the preferredmethod involves preprocessing through a first derivative with a widesmoothing window (e.g., 31 nm), scatter correction throughmultiplicative scatter correction or standard normal variatetransformation [R. Barnes, M. Dhanoa, S. Lister, Applied Spectroscopy,43:772-777 (1989], and wavelength selection in the range 1450-1850 nm ora subset thereof. In addition, information from a water band, such as1180-1450 nm may also be included. The preprocessed data is corrected tothe tissue template and partial-least squares is applied to develop thecalibration model. Glucose is then measured through the application ofthe identical preprocessing steps to a spectral measurement (firstderivative, scatter correction, wavelength selection and tissue templatecorrection) to obtain the processed spectral measurement, x. The glucosemeasurement associated with the spectral measurement is determinedaccording to

ŷ=xG+b  (13)

[0127] where G∈

^(M×1) is a linear transformation, derived from partial least-squaresregression that represents both the feature extraction step and thecalibration model.

[0128] While the invention has been described herein with respect tomeasurement of glucose in blood and tissue, the principles of theinvention find application in detection of other tissue constituents andanalytes as well.

[0129] Although the invention has been described herein with referenceto certain preferred embodiments, one skilled in the art will readilyappreciate that other applications may be substituted for those setforth herein without departing from the spirit and scope of the presentinvention. Accordingly, the invention should only be limited by theclaims included below.

1. A method for noninvasive measurement of a target analyte in a tissuesample, comprising the steps of: measuring a spectrum of a tissuesample; detecting changes in optical properties of said tissue samplerelated to physiological changes in said tissue, as manifested byspectral features reflecting said changes; and either correcting adirect analyte measurement on the basis of said detected changes; ormeasuring said analyte indirectly on the basis of said spectralfeatures.
 2. The method of claim 1, wherein said step of measuring aspectrum comprises measuring a spectrum, m, of said tissue, saidspectrum denoted by a vector m∈

^(1×N) of absorbance values pertaining to a set of N wavelengths, λ∈

^(N) spanning near IR region of approximately 700 to 2500 nm.
 3. Themethod of claim 2, wherein m comprises a set of wavelengths in the nearIR that have been optimized for extraction of features.
 4. The method ofclaim 1, further comprising the step of preprocessing said spectrum,preprocessing comprising any of: filtering; calculating a first orsecond derivative of said spectrum; and scatter correction.
 4. Themethod of claim 1, further comprising the step of extracting saidfeatures, feature extraction comprising any mathematical transformationthat enhances a quality or aspect of a sample measurement forinterpretation so that structural properties and physiological state ofsaid tissue sample are concisely represented.
 5. The method of claim 4,wherein said feature extraction step comprises the steps of: identifyingdistinct absorption bands that change in differing manners with respectto changes in pathlength, scattering and absorption properties of thesample; developing a set of features that represents or reflects theoptical properties of the tissue; and applying said features toidentify: conditions suitable for analyte measurement; and conditionsunsuitable for analyte measurement.
 6. The method of claim 4, whereinfeatures include any of: simple features, comprising values of aspectral measurement or a processed spectral measurement at criticalpoints, wherein a critical point is a point having a slope of 0; derivedfeatures, comprising features derived from simple features through amathematical transformation; and abstract features; wherein simple andderived features generally have a physical interpretation, and whereinabstract features do not necessarily have a specific interpretationrelated to a physical system.
 7. The method of claim 6, wherein abstractfeatures comprise scores from a principal component analysis, whereinleading principal components represent variation related to thestructural properties and physiological state of the tissue sample, sothat the optical properties of the tissue are represented.
 8. The methodof claim 6, wherein features are determined from a second derivative ofsaid spectrum, wherein value of said second derivative spectrum at eachcritical point constitutes a feature that represents a key property ofthe sample.
 9. The method of claim 8, wherein features include:normalization points; fat band points; protein band points; and waterband points; wherein normalization points are used to determine derivedfeatures, and fat, protein and water points are respectively located invicinity of an absorption band due to fat, protein or water.
 10. Themethod of claim 1, further comprising the step of: determiningdifference between a tissue template and a preprocessed spectrumaccording to: z=x−(cx _(t) +d); wherein x comprises a pre-processedspectrum or a selected set of features, x_(t) comprises a tissuetemplate associated with a measurement period, and c and d are slope andintercept adjustments to the tissue template.
 11. The method of claim10, wherein said tissue template is determined through one or morespectral measurements combined according to a predetermined dataselection criterion during each measurement period,
 12. The method ofclaim 11, wherein a measurement period comprises a time period duringwhich the state of the tissue sample is uniform and measurement site isconstant.
 13. The method of claim 11, further comprising the step of:providing an associated set of reference analyte values, said valuescombined according to said predetermined data selection criterion toform a measurement bias adjustment.
 14. The method of claim 10, whereinsaid tissue template comprises any set of features from a given subjector calibration set that future spectral measurements will be comparedwith, wherein c and d are determined are determined throughleast-squares fit of the tissue template over a particular wavelengthrange to the measured spectrum.
 15. The method of claim 1, furthercomprising any of the steps of: detecting conditions not conducive toanalyte measurement; and detecting outliers.
 16. The method of claim 15,said step of detecting conditions not conducive to analyte measurementcomprising the steps of: monitoring selected features; and ensuring thattheir variation over a given measurement does not exceed that of acalibration set or another previously established limit.
 17. The methodof claim 1, further comprising the step of providing a calibrationmodel, said model determined from a calibration set of exemplary paireddata points each consisting of a preprocessed spectral measurement, xand an associated reference analyte value, y, said calibration setincluding analyte concentrations that span the expected range ofvariation and spectral variation representative of future spectralmeasurements, said model comprising an equation, a set of parameters andcorresponding computer code implemented to measure a subject's analytelevel on the basis of a processed spectral measurement.
 18. The methodof claim 17, wherein said y values are determined from samples of blood,serum, plasma or interstitial fluid taken from a fingertip, a site nearthe measurement site or an alternate site.
 19. The method of claim 18,wherein said alternate site comprises a sample site that has beendesigned or determined to reflect the sample site.
 20. The method ofclaim 17, wherein said parameters are calculated using any of:multivariate regression; weighted multivariate regression; principalcomponent regression; parital least squares regression; and artificialneural networks.
 21. The method of claim 17, wherein said step ofcorrecting a direct analyte measurement on the basis of said detectedchanges comprises supplementing said model with selected featuresaccording to: ŷ=f(x _(p) ,z)+b, where ŷ is an estimated glucoseconcentration, x_(p)∈

^(N) is a processed spectral measurement, z∈

^(M) is a set of features representative of the physiological state oroptical properties of the tissue, f:

^(N,M)→

¹ is a model used to measure glucose on the basis of a preprocessedspectrum and extracted features, and b is a baseline adjustment forglucose measurement associated with both a tissue template and saidcalibration model.
 22. The method of claim 17, wherein said step ofcorrecting a direct analyte measurement on the basis of said detectedchanges comprises supplementing said model with selected featuresaccording to: ŷ=f(x _(p))−(m _(s) g(z)+m _(i))+b, where ŷ is anestimated glucose concentration, x_(p)∈

^(N) is a processed spectral measurement, z∈

^(M) is a set of features representative of the physiological state oroptical properties of the tissue, wherein x_(p) and z are independent,where f:

^(N)→

¹ is a model used to measure glucose in the absence of physiological orother tissue variation, g:

^(M)→

¹ is a model used to map the features to a variable correlated to errorin glucose measurement caused by a change in the optical properties ofthe tissue, m_(s) and m_(i) are slope and intercepts used to convertg(z) to correct units, and b is a baseline adjustment for glucosemeasurement associated with both a tissue template and said calibrationmodel.
 23. The method of claim 22, wherein f(•) and g(•) are separatelydetermined experimentally, wherein f (•) is determined by manipulatingglucose while tissue optical properties remain constant, and wherein theoptical properties of tissue are allowed to fluctuate and g(•), m_(s)and m_(i) are determined on the basis of the error in glucosemeasurement where target value for g(?) is given by r=y−f(x _(p))−bwhere y is a reference glucose concentration.
 24. The method of claim23, wherein said step of correcting a direct analyte measurement on thebasis of said detected changes comprises supplementing said model withselected features according to: ŷ=x _(p) F−(m _(s) zG+m _(i))+b, whereinf(•) and g(•) are determined to be linear over range of measurement andwhere F∈

^(N×1) and G∈

^(M×1).
 25. The method of claim 17, wherein said step of measuring saidanalyte indirectly on the basis of said spectral features comprisesusing extracted features to measure glucose indirectly according to:ŷ=(m _(s) g(z)+m _(i))+b where g:

^(M)→

¹ comprises said model, said model used to map set of features z to avariable correlated to a reference glucose level and m_(s) and m_(i) areslope and intercepts used to convert g(z) to the correct units and b isa baseline adjustment for glucose measurement.
 26. The method of claim25, wherein features are selected based on their combined correlation tothe reference glucose concentration.
 27. The method of claim 26, whereinfeatures are selected based on any of: a priori knowledge;trial-and-error; stepwise regression; random search techniques; geneticalgorithms; and evolutionary programming.
 28. The method of claim 26,wherein g(•) is determined according to: ŷ=(m _(s) zG+m _(i))+b where G∈

^(M×1).
 29. The method of claim 25, wherein z includes either anormalized second derivative fat band or protein band, and whereinm_(s,) m_(i) and G are determined through any of multivariateregression, weighted multivariate regression and locally weightedregression.
 30. The method of claim 25, wherein said step of measuringsaid analyte indirectly on the basis of said spectral features comprisesusing extracted features to measure glucose indirectly according to: ŷ=g₁ z ₁ +g ₂ z ₂ +g ₃ z ₃ +g ₄ +b, where z₁, z₂ and z₃ comprise normalizedsecond derivative values of a fat band and two water bands,respectively.
 31. The method of claim 1, wherein said step of measuringsaid analyte indirectly comprises: providing a noninvasive glucosecalibration model wherein abstract features that reflect said changes inoptical properties of said tissue are used as independent variables forsaid calibration; preprocessing said measured spectrum; and decomposingsaid preprocessed spectrum according to: z=xP where x∈

^(1×N) is the preprocessed spectrum, N is number of wavelengths selectedfor calibration, P∈

^(1×M) is a set of M eigenvectors or loadings obtained from a principalcomponents analysis of a calibration set and z∈

^(1×M) is the set of abstract features used to measure glucose throughapplication of said calibration model, wherein said model is eitherlinear or nonlinear.
 32. The method of claim 31, wherein said abstractfeatures comprise scores from a principal component analysis.
 33. Themethod of claim 31, wherein providing said calibration model comprisesthe steps of: preprocessing spectral measurements from said calibrationset through a first derivative with a wide smoothing window; scattercorrecting said preprocessed measurements from said calibration setthrough multiplicative scatter correction or standard normal variatetransformation; selecting wavelengths from a range of approximately1450-1850 nm and optionally 1180-1450 nm; correcting said preprocessed,scatter-corrected data to a tissue template; and applying partial leastsquares regression.
 34. The method of claim 33, further comprising thesteps of: applying identical processing to said measured spectrum asapplied in developing said calibration model.
 35. The method of claim34, further comprising the step of: determining a glucose measurementaccording to: ŷ=mG+b where G∈

^(M×1) is the calibration model derived from partial least-squaresregression and b is a baseline correction.
 36. An apparatus fornoninvasive measurement of a target analyte in a tissue samplecomprising: means for measuring a spectrum of a tissue sample; means fordetecting changes in optical properties of said tissue sample related tophysiological changes in said tissue, as manifested by spectral featuresreflecting said changes; and either correcting a direct analytemeasurement on the basis of said detected changes; or measuring saidanalyte indirectly on the basis of said spectral features.
 37. Theapparatus of claim 36, wherein said means for measuring a spectrum of atissue sample comprises a spectrometer system, said spectrometer systemcomprising: a source of near infrared (NIR) radiation; a wavelengthselection element; a means for interfacing with the measurement site onthe skin of a subject, wherein radiation is directed toward themeasurement site from the source, and a light signal returned from thesite is collected; means for detecting said returned radiation; andmeans for digitizing said returned signal.
 38. The apparatus of claim37, wherein said NIR source radiates energy in a range of approximately700-2500 nm.
 39. The apparatus of claim 37, wherein said NIR sourcecomprises any of: an LED array; a halogen lamp.
 40. The apparatus ofclaim 37, further comprising a band pass filter to minimize effect ofwavelengths radiated from said source that are outside a spectral rangeof interest.
 41. The apparatus of claim 37, wherein said wavelengthselection element comprises any of: a dispersive element; aninterferometer; and successive illumination of elements of an LED array.42. The apparatus of claim 41, further comprising a reference wavelengthstandard, wherein changes in said wavelength selection element caused byenvironmental changes are compensated by scanning said standardproximate to or simultaneous with interrogation of said tissue.
 43. Theapparatus of claim 37, wherein said means for interfacing with themeasurement site on the skin of a subject comprises: at least oneoptical element that directs radiation to and/or from the tissue, saidoptical element including any of: a light pipe; a fiber optic; a lenssystem; and a light directing mirror system.
 44. The apparatus of claim43, said means for interfacing with the measurement site on the skin ofa subject further comprising: a guide to assist in interfacing saidoptical element and said measurement site.
 45. The apparatus of claim44, said means for interfacing with the measurement site furthercomprising a subject interface module, said subject interface moduleincluding at least an elbow rest and a wrist rest.
 46. The apparatus ofclaim 43, said means for interfacing with the measurement site on theskin of a subject further comprising: an optical coupling fluid, aportion of said fluid being interposed between said measurement site andsaid source to minimize specular reflectance from surface of the skin.47. The apparatus of claim 37, wherein an area irradiated and an areafrom which returning radiation is collected from are separated by apreselected distance, said distance selected to target a tissue volumeconducive to measurement of a property of interest.
 48. The apparatus ofclaim 37, wherein said means for detecting said returned radiationcomprises: single diodes or diode arrays responsive to targetedwavelengths of interest.
 49. The apparatus of claim 48, wherein saiddiodes include InGaAs detectors.
 50. The apparatus of claim 48, whereinsaid diodes are selected such that material junctions in said diodes donot coincide with targeted wavelengths.
 51. The apparatus of claim 48,wherein said diodes convert said signal to a voltage.
 52. The apparatusof claim 37, said means for digitizing said returned signal comprises anADC (analog-to-digital converter), wherein said signal, having beenconverted to a voltage, is digitally sampled for analysis on amicroprocessor-based system.
 53. The apparatus of claim 52, furthercomprising a display, wherein a result of said analysis is displayed.54. The apparatus of claim 36, wherein said means for detecting changesin optical properties of said tissue sample related to physiologicalchanges in said tissue, as manifested by spectral features reflectingsaid changes; and either correcting a direct analyte measurement on thebasis of said detected changes; or measuring said analyte indirectly onthe basis of said spectral features comprises an analyzer, said analyzerincluding: means for measuring a spectrum, m of said tissue, saidspectrum denoted by a vector m∈

^(1×N) of absorbance values pertaining to a set of N wavelengths, λ∈

^(N) spanning near IR region of approximately 700 to 2500 nm.
 55. Theapparatus of claim 54, said analyzer further comprising: means forpreprocessing said spectrum.
 56. The apparatus of claim 55, saidanalyzer further comprising: means for extracting said features,comprising any mathematical transformation that enhances a quality oraspect of a sample measurement for interpretation so that structuralproperties and physiological state of said tissue sample are conciselyrepresented.
 57. The apparatus of claim 56, said analyzer furthercomprising: a tissue template determined through one or more spectralmeasurements combined according to a predetermined data selectioncriterion during each a measurement period, said tissue templatesubtracted from said spectrum.
 58. The apparatus of claim 57, saidanalyzer further comprising means for calculating a measurement biasadjustment.
 59. The apparatus of claim 58, said analyzer furthercomprising means for detecting outliers and conditions detrimental tospectroscopic glucose measurement though selected features.
 60. Theapparatus of claim 59, said analyzer further comprising: at least onecalibration model, said model determined from a calibration set ofexemplary paired data points each consisting of a spectral measurement,x and an associated reference analyte value, y, said calibration setincluding analyte concentrations that span the expected range ofvariation and spectral variation representative of future spectralmeasurements, said model comprising an equation, a set of parameters andcorresponding computer code implemented to measure a subject's analytelevel on the basis of a processed spectral measurement.